[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119388-en":3,"doc-seo-119388-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119388,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Advanced Techniques for Anomaly Detection in Blockchain - Leveraging Clustering and Machine Learning","Blockchain technology has revolutionized data security and transaction transparency across industries, yet network complexity increasingly generates anomalies that demand systematic investigation. This study analyzes blockchain anomalies through machine learning approaches, comparing supervised and unsupervised anomaly detection techniques for their ability to identify irregular patterns. Results show that machine learning models can detect anomalies with high accuracy, offering actionable insights into possible threats and system vulnerabilities. The work supports stronger monitoring and improved blockchain security.","Journal of Information Systems and Informatics  \nVol. 7, No. 1, March 2025 e-ISSN: 2656-4882 p-ISSN: 2656-5935  \nDOI: 10.51519/journalisi.v7i1.1047 Published By DRPM-UBD  \nAdvanced Techniques for Anomaly Detection in Blockchain: Leveraging Clustering and Machine Learning  \nFerdiansyah1, Usman Ependi2,*, Tasmi3, Muhammad Haikal4, Mikko5  \n1,3,4,5 Faculty of Computer and Science, Universitas Indo Global Mandiri, Palembang, Indonesia  \n2Informatics Department, Bina Darma University, Palembang, Indonesia  \n[Email:](Email:1 ferdi@uigm.ac.id)[1](Email:1 ferdi@uigm.ac.id)[ ferdi@uigm.ac.id](Email:1 ferdi@uigm.ac.id),2,*[u.ependi@binadarma.ac.id](u.ependi@binadarma.ac.id), [3](3 tasmi@uigm.ac.id)[ tasmi@uigm.ac.id](3 tasmi@uigm.ac.id),  \n[4](4 2022310006@student.uigm.ac.id)[ 2022310006@student.uigm.ac.id](4 2022310006@student.uigm.ac.id), [5](5 202231070@student.uigm.ac.id)[ 202231070@student.uigm.ac.id](5 202231070@student.uigm.ac.id)  \nAbstract  \nBlockchain technology has revolutionized data security and transaction transparency across various industries. However, the increasing complexity of blockchain networks has led to anomalies that require further investigation. This study aims to analyze anomalies in blockchain systems using machine learning approaches. Various anomaly detection techniques, including supervised and unsupervised methods, are evaluated for their effectiveness in identifying irregularities. The results indicate that machine learning models can detect anomalies with high accuracy, providing insights into potential threats and system vulnerabilities. The findings of this research contribute to improving blockchain security and developing more robust monitoring systems.  \nKeywords: Blockchain Security, Anomaly Detection, Machine Learning, Fraud Detection.  \n1. INTRODUCTION  \nBlockchain has emerged as one of the most revolutionary technologies in the past decade, with wide-ranging applications from cryptocurrency to supply chain management[1] . The technology has revolutionized data security and transaction transparency across various industries [2], [3]. However, the increasing complexity of blockchain networks has led to anomalies that require further investigation. Traditional anomaly detection techniques often fall short in identifying hidden anomaly patterns within large and diverse datasets due to the distributed and encrypted nature of blockchain data. This inadequacy poses significant risks, including potential fraud, security breaches, and system vulnerabilities[4] .  \nThe security and integrity of data stored within blockchain networks are crucial to ensuring the trust and reliability of these systems [5]. However, with the increasing adoption of this technology, the threats to blockchain network security have also escalated, including suspicious transactions and other anomalous activities [6] . Anomaly detection in blockchain networks is a complex and challenging process due to the distributed and encrypted nature of the data[7] . Traditional techniques  \n479  \nThis work is licensed under a Creative Commons Attribution 4.0 International License.  \np-ISSN: 2656-5935 [http://journal-isi.org/index.php/isi](http://journal-isi.org/index.php/isi) e-ISSN: 2656-4882  \noften fall short in identifying hidden anomaly patterns within large and diverse datasets. Therefore, more sophisticated approaches are required to effectively detect anomalies.  \nA recent study successfully detected anomalies within blockchain transactions using machine learning classifiers and explainability analysis. The integration of eXplainable Artificial Intelligence (XAI) techniques with tree-based ensemble classifiers, such as the Shapley Additive exPlanation (SHAP) method, enhanced the detection of anomalous Bitcoin transactions[8] . This approach improved the True Positive Rate (TPR) and ROC-AUC scores, demonstrating the effectiveness of advanced machine learning techniques in anomaly detection [9] . there have been instances where anomal","cbCairEgpsjAUHZK","https://ap.wps.com/l/cbCairEgpsjAUHZK","pdf",551054,1,14,"English","en",105,"# Abstract\n# Introduction\n# Related Work\n# Methodology\n## Clustering Algorithms (K-Means)\n## Machine Learning Anomaly Detection (Random Forest)\n# Results and Discussion\n# Conclusion","[{\"question\":\"Why is anomaly detection in blockchain networks challenging?\",\"answer\":\"Blockchain data is distributed and encrypted, and illicit transactions may be infrequent. Traditional techniques often struggle to uncover hidden anomaly patterns in large, diverse datasets.\"},{\"question\":\"What machine learning approaches are evaluated for detecting blockchain anomalies?\",\"answer\":\"The study evaluates both supervised and unsupervised anomaly detection techniques, using clustering algorithms such as K-Means and anomaly detection models such as Random Forest.\"},{\"question\":\"How do the proposed techniques improve anomaly detection outcomes?\",\"answer\":\"Clustering groups transactions by feature similarity, while machine learning classifiers identify suspicious irregularities. The approach is intended to increase accuracy and efficiency and provide better insights for monitoring.\"}]","Advanced Techniques for Anomaly Detection in Blockchain - Leveraging Clustering and Machine Learning | PDF",1785724053,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"advanced-techniques-for-anomaly-detection-in-blockchain-leveraging-clustering-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/advanced-techniques-for-anomaly-detection-in-blockchain-leveraging-clustering-and-machine-learning/119388/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is anomaly detection in blockchain networks challenging?","Question",{"text":75,"@type":76},"Blockchain data is distributed and encrypted, and illicit transactions may be infrequent. Traditional techniques often struggle to uncover hidden anomaly patterns in large, diverse datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approaches are evaluated for detecting blockchain anomalies?",{"text":80,"@type":76},"The study evaluates both supervised and unsupervised anomaly detection techniques, using clustering algorithms such as K-Means and anomaly detection models such as Random Forest.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the proposed techniques improve anomaly detection outcomes?",{"text":84,"@type":76},"Clustering groups transactions by feature similarity, while machine learning classifiers identify suspicious irregularities. The approach is intended to increase accuracy and efficiency and provide better insights for monitoring.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]